Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks

Jaseena, K U; Kovoor, Binsu C.

doi:10.1016/j.enconman.2021.113944

Cited by 167 publications

(24 citation statements)

References 44 publications

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“…Jaseena and Kovoor [95] suggested a decomposition strategy-based hybrid forecasting approach for wind power generation using Bi-LSTM technique. The authors used the EMD, EEMD, WT and Empirical Wavelet Transform (EWT) to denoise wind speed data into several high and lowfrequency signals.…”

Section: ) Long Short-term Memory-based Hybrid Approachmentioning

confidence: 99%

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Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

et al. 2021

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Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation.INDEX TERMS Wind power forecasting, artificial intelligence, machine learning, deep learning, optimization, hybrid approaches.

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Section: ) Long Short-term Memory-based Hybrid Approachmentioning

confidence: 99%

“…Sewdien et al [28] designed the hybrid AI model with RF and DBN. Jaseena and Kovoor [95] utilized EWT and Bi-LSTM to build a hybrid AI framework. Kartite and Cherkaoui [38] introduced a hybrid AI model using VMD and CNN.…”

Section: E Ai Hybridization Issuesmentioning

confidence: 99%

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

et al. 2021

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“…The CLSTM model, constructed by integrating CNN and LSTM, had been used elsewhere in natural language processing where emotions were analysed with text inputs [49], in speech processing where voice search tasks were performed using CLDNN combining CNN, LSTM and DNN [50], in video processing with CNN and Bidirectional LSTM models built to recognize human actions in video sequences [51], in the medical area where the CNN-LSTM method was developed to detect arrhythmias in electrocardiograms [52] and in industrial areas where a convolutional bi-directional LSTM model was designed to predict tool wearing [53]. Other studies with CLSTM are evident, for example, time series application for prediction of residential energy consumption [54] [55], solar radiation prediction [43,[56][57][58] and wind speed prediction [59][60][61] as well as stock market applications in the prediction of share prices [62,63]. In the solar radiation forecasting area, the study of Ghimire et al [43] has developed a CLSTM model and compared its performance against the CNN, LSTM and DNN-based models,…”

Section: Theoretical Overviewmentioning

confidence: 99%

Forecasting Photosynthetic Photon Flux Density Under Cloud Effects: Novel Predictive Model Using Convolutional Neural Network Integrated With Long Short-term Memory Network

Deo

Grant

Webb

et al. 2021

Preprint

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Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-minute) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a Total Sky Imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m-2 s-1, mean absolute error MAE = 150.24 μ mol of photons m-2 s-1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate & McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.

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“…GRU is a variant of long shor-term memory (LSTM), which is effective in modeling long-term dependencies of sequential data. LSTM has been successfully applied in many applications of power and renewable energy systems, such as wind speed forecasting [29][30][31][32]. Compared to LSTM, GRU can achieve comparable performance with a simpler architecture and fewer tensor computations [33].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning

Wang

et al. 2021

Energies

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The proliferation of distributed renewable energy resources (RESs) poses major challenges to the operation of microgrids due to uncertainty. Traditional online scheduling approaches relying on accurate forecasts become difficult to implement due to the increase of uncertain RESs. Although several data-driven methods have been proposed recently to overcome the challenge, they generally suffer from a scalability issue due to the limited ability to optimize high-dimensional continuous control variables. To address these issues, we propose a data-driven online scheduling method for microgrid energy optimization based on continuous-control deep reinforcement learning (DRL). We formulate the online scheduling problem as a Markov decision process (MDP). The objective is to minimize the operating cost of the microgrid considering the uncertainty of RESs generation, load demand, and electricity prices. To learn the optimal scheduling strategy, a Gated Recurrent Unit (GRU)-based network is designed to extract temporal features of uncertainty and generate the optimal scheduling decisions in an end-to-end manner. To optimize the policy with high-dimensional and continuous actions, proximal policy optimization (PPO) is employed to train the neural network-based policy in a data-driven fashion. The proposed method does not require any forecasting information on the uncertainty or a prior knowledge of the physical model of the microgrid. Simulation results using realistic power system data of California Independent System Operator (CAISO) demonstrate the effectiveness of the proposed method.

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Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks

Cited by 167 publications

References 44 publications

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Forecasting Photosynthetic Photon Flux Density Under Cloud Effects: Novel Predictive Model Using Convolutional Neural Network Integrated With Long Short-term Memory Network

Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning

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